Modern computing systems are overwhelmingly designed to move data to computation. This
design choice goes directly against at least three key trends in computing that cause …

Many modern and emerging applications must process increasingly large volumes of data.
Unfortunately, prevalent computing paradigms are not designed to efficiently handle such …

Data movement between the CPU and main memory is a first-order obstacle against improv
ing performance, scalability, and energy efficiency in modern systems. Computer systems …

Main memory, composed of DRAM, is a performance bottleneck for many applications, due
to the high DRAM access latency. In-DRAM caches work to mitigate this latency by …

Important workloads, such as machine learning and graph analytics applications, heavily
involve sparse linear algebra operations. These operations use sparse matrix compression …

The rapidly growing popularity and scale of data-parallel workloads demand a
corresponding increase in raw computational power of Graphics Processing Units (GPUs) …

The massive parallelism present in GPUs comes at the cost of reduced L1 and L2 cache
sizes per thread, leading to serious cache contention problems such as thrashing. Hence …

Because of severe limitations in technology scaling, architects have innovated in
specializing general purpose processors for computation primitives (eg vector instructions …

Increasing communication overheads are already threatening computer system scaling. One
approach to dramatically reduce communication overheads is waferscale processing …

As Moore's law slows down, GPUs must pivot towards multi-module designs to continue
scaling performance at historical rates. Prior work on multi-module GPUs has focused on …